994 research outputs found
Equilibrium and Learning in Queues with Advance Reservations
Consider a multi-class preemptive-resume queueing system that
supports advance reservations (AR). In this system, strategic customers must
decide whether to reserve a server in advance (thereby gaining higher priority)
or avoid AR. Reserving a server in advance bears a cost. In this paper, we
conduct a game-theoretic analysis of this system, characterizing the
equilibrium strategies. Specifically, we show that the game has two types of
equilibria. In one type, none of the customers makes reservation. In the other
type, only customers that realize early enough that they will need service make
reservations. We show that the types and number of equilibria depend on the
parameters of the queue and on the reservation cost. Specifically, we prove
that the equilibrium is unique if the server utilization is below 1/2.
Otherwise, there may be multiple equilibria depending on the reservation cost.
Next, we assume that the reservation cost is a fee set by the provider. In that
case, we show that the revenue maximizing fee leads to a unique equilibrium if
the utilization is below 2/3, but multiple equilibria if the utilization
exceeds 2/3. Finally, we study a dynamic version of the game, where users learn
and adapt their strategies based on observations of past actions or strategies
of other users. Depending on the type of learning (i.e., action learning vs.\
strategy learning), we show that the game converges to an equilibrium in some
cases, while it cycles in other cases
Strategies for a centralized single product multiclass M/G/1 make-to-stock queue
Make-to-stock queues are typically investigated in the M/M/1 settings. For centralized single-item systems with backlogs, the multilevel rationing (MR) policy is established as optimal and the strict priority (SP) policy is a practical compromise, balancing cost and ease of implementation. However, the optimal policy is unknown when service time is general, i.e., for M/G/1 queues. Dynamic programming, the tool commonly used to investigate the MR policy in make-to-stock queues, is less practical when service time is general. In this paper we focus on customer composition: the proportion of customers of each class to the total number of customers in the queue. We do so because the number of customers in M/G/1 queues is invariant for any nonidling and nonanticipating policy. To characterize customer composition, we consider a series of two-priority M/G/1 queues where the first service time in each busy period is different from standard service times, i.e., this first service time is exceptional. We characterize the required exceptional first service times and the exact solution of such queues. From our results, we derive the optimal cost and control for the MR and SP policies for M/G/1 make-to-stock queues
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